Terrain detection and segmentation for autonomous vehicle navigation: A state-of-the-art systematic review

This review comprehensively investigates the current state and emerging trends of autonomous vehicle terrain detection and segmentation. By systematically reviewing literature from various databases, this study outlines the evolution of detection and segmentation techniques from traditional computer vision methods to advanced machine learning and deep learning approaches. It identifies critical technological advancements, evaluates their performance, and discusses the challenges faced under various environmental conditions, data acquisition, and integration with vehicle systems. This study also highlights the need for standardized benchmarks and datasets to facilitate the development and testing of robust terrain detection systems. This review encompasses terrain detection and segmentation in structured environments, such as urban roads and highways, and unstructured environments, including rural paths and off-road terrains, to comprehensively analyze autonomous vehicle navigation challenges. By analyzing recent research findings, this review provides insights into future directions for overcoming these limitations and fostering innovation in the autonomous driving domain. • Explores terrain detection and segmentation technologies in autonomous vehicles. • Evaluates current methods’ effectiveness and limitations across environments. • Identifies challenges in advancing terrain detection and segmentation approaches. • Highlights trends and solutions for future research directions.